In order to realize a full parametric control of motion style, we exploited the Bayesian motion morphing technique to create a continuous expression space that smoothly interpolates between human and monkey expressions. We used two human expressions and two monkey expressions as basic patterns, which represented corresponding emotional states (‘fear’ and ‘anger/threat’). Interpolating between these four prototypical motions in five equidistant steps, we generated a set of 25 facial movements that vary in five steps along two dimensions, the expression type, and the species.. Each generated motion pattern can be parameterized by a two-dimensional style vector (e, s), where the first component e specifies the expression type (e1⁄40: expression 1 (‘fear’) and e1⁄41: expression 2 (‘anger/threat’)), and where the second variable s defines the species-specificity of the motion (s 1⁄4 0: monkey and s 1⁄4 1: human).
Our result implies that primate facial expressions are perceptually encoded largely independently of the head shape (human vs. monkey) and of the stimulus view. Especially, this implies substantial independence of this encoding of the two-dimensional image features, which vary substantially between the view conditions, and even more between the human and the monkey avatar model.
Interesting predictions emerged when the model is tested with the stimuli of variable expression strength, which were generated by morphing between the prototypes and neutral facial expressions. Here the Face neurons as well as the Expression neurons in the norm-based model show a gradual, almost linear variation of their activations with the expression level.Publications
Encoding of dynamic facial expressions in the macaque superior temporal sulcus
Shape-invariant encoding of dynamic primate facial expressions in human perception
Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition.